yNational Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom

Central Illustration

Abstract

Background Genomic analyses have suggested that the LPA gene and its associated plasma biomarker, lipoprotein(a) (Lp[a]), represent a causal risk factor for coronary heart disease (CHD). As such, lowering Lp(a) levels has emerged as a therapeutic strategy. Beyond target identification, human genetics may contribute to the development of new therapies by defining the full spectrum of beneficial and adverse consequences and by developing a dose–response curve of target perturbation.

Objectives The goal of this study was to establish the full phenotypic impact of LPA gene variation and to estimate a dose–response curve between genetically altered plasma Lp(a) and risk for CHD.

Methods We leveraged genetic variants at the LPA gene from 3 data sources: individual-level data from 112,338 participants in the U.K. Biobank; summary association results from large-scale genome-wide association studies; and LPA gene sequencing results from case subjects with CHD and control subjects free of CHD.

Lipoprotein(a) (Lp[a]) is a circulating lipoprotein in which the constituent apolipoprotein B on a low-density lipoprotein (LDL) particle is modified by the covalent addition of another protein, namely apolipoprotein(a) (1,2). Higher plasma Lp(a) levels are associated with an increased risk for incident coronary heart disease (CHD) (3), heritable, and largely determined by variation in the LPA gene, which encodes apolipoprotein(a) (2). Genetic variants in LPA that increase Lp(a) levels also increase CHD risk, suggesting that Lp(a) is a causal risk factor for development of CHD (4–6). Consequently, lowering Lp(a) levels has emerged as a therapeutic strategy to reduce the risk of CHD (2,7).

Beyond identifying a therapeutic target gene, human genetics may help estimate the probable efficacy and safety of pharmacological modulation (8). Although LPA variants have been consistently reported to be associated with CHD (5,6) and aortic valve stenosis (9,10), there is uncertainty around the full spectrum of phenotypic consequences. Previous studies have reported conflicting evidence on whether LPA variants are associated with other cardiovascular diseases, such as stroke (11,12). In addition, observational epidemiology has associated lower plasma levels of Lp(a) with increased risks of cancer (13) and diabetes (14).

Deoxyribonucleic acid (DNA) sequence variants might also provide a mechanism to estimate a dose–response curve. In particular, the simultaneous identification of gain-of-function variants as well as loss-of-function variants and an analysis of phenotypic effects can reveal a dose–response curve even before a clinical trial is initiated.

In the present study, we leveraged genetic variants across the allele frequency spectrum and 3 large data sources to evaluate the phenotypic consequences of genetically lowered Lp(a) levels. The effect of a genetically mediated 1 SD decrease in Lp(a) levels on cardiometabolic disease and range of other disorders was estimated.

Methods

The overall study design is shown in Figure 1. Several data sources were leveraged to provide greater power for estimating the effect of genetically lowered Lp(a) level on cardiometabolic traits and outcomes, to conduct a phenome-wide association study, and to examine the effect of rare loss-of-function variants in the LPA gene on risk of CHD.

DNA sequence variants

To estimate the effect of genetically lowered Lp(a) level on a wide range of phenotypes, individual-level data from U.K. Biobank were combined with summary-level data from large-scale GWAS. Four single nucleotide polymorphisms (SNPs) in the LPA gene were used that have been previously associated with plasma Lp(a) levels: rs10455872, rs3798220, rs41272114, and rs143431368 (Online Table 3). Together, rs10455872 and rs3798220 explain approximately 36% of variation in plasma Lp(a) levels (5); the other 2 (rs41272114 and rs143431368) are loss-of-function variants associated with lower Lp(a) levels.

To standardize the estimates to a 1 SD decrease in Lp(a) levels, estimates of the effect of each variant on Lp(a) levels from the ARIC (Atherosclerosis Risk In Communities) study were used (Online Table 3, Online Appendix). ARIC is a community-based study of 15,792 white and black participants, ages 45 to 64 years, who were first enrolled in 1987 (24). The analysis was restricted to 2,758 individuals of European ancestry in the ARIC cohort who had Lp(a) levels measured at the baseline visit by using a double-antibody enzyme-linked immunosorbent assay (25). Participants fasted for 12 to 24 h before blood collection. Plasma was separated from cells with centrifugation within 1 h of collection and stored at –70°C. Analyses were performed within 2 weeks. The assay was shown to have high internal reliability in a validation study in ARIC (r = 0.90) and in a separate comparison versus a newer assay calibrated by using International Federation of Clinical Chemistry reference material (r = 0.88) (26). Linear regression was used, adjusting for age, sex, and 5 principal components of ancestry, to estimate the association between each variant and Lp(a) level in an additive model. Because Lp(a) levels were non-normally distributed, log-transformed Lp(a) levels were used, as previously described (5).

Statistical analysis

For analyses of both U.K. Biobank and summary-level data, a gene variant score was created out of the 4 SNPs. For each variant, we modeled the Lp(a)-lowering allele and weighted by the effect of each SNP on log-transformed Lp(a) levels in SD units (Online Table 3). The effect of this gene variant score on each trait and outcome was then examined, standardized per SD decrease in log-transformed Lp(a) levels.

For U.K. Biobank, an LPA gene variant score was generated in units of SD Lp(a) by multiplying each variant by its effect on Lp(a) levels. This gene variant score was then included in a logistic regression analysis adjusting for age, sex, 10 principal components of ancestry, and a dummy variable for array type. For the summary-level data, this approach is equivalent to an inverse variance–weighted, fixed effects meta-analysis of the effect of each variant on a trait or outcome of interest, divided by the effect of each variant on Lp(a) levels (27).

For the primary outcomes (the 9 cardiometabolic diseases), a Bonferroni-adjusted level of significance of p = 0.05/9 = 0.0056 was set. For the secondary analysis of cardiometabolic traits, which included 15 traits, a level of significance of p = 0.05/15 = 0.003 was set. For the phenome-wide association study of 28 phenotypes, a level of significance of p = 0.05/28 = 0.0018 was set.

Loss-of-function variant analysis

To examine whether loss-of-function variants in the LPA gene influence CHD risk, whole exome sequencing data from the MIGen Consortium were used (Online Appendix). This consortium is composed of 10 coronary artery disease case-control studies (28,29). Loss-of-function variants were defined as follows: 1) nonsense mutations that resulted in early termination of the apolipoprotein(a) protein; 2) frameshift mutations due to insertions or deletions of DNA; or 3) splice-site mutations that resulted in an incorrectly spliced protein. These loss-of-function variants in the MIGen Consortium were combined with loss-of-function variants that were genotyped (either directly or imputed) in the U.K. Biobank. Variants are provided in Online Tables 4 and 5. We analyzed rare variants (<1%) separately to a common loss-of-function variant in the LPA gene (rs41272114, allele frequency of 3.8% in U.K. Biobank) (30,31).

We tested for the association of CHD with the presence of a loss-of-function variant using logistic regression. In the MIGen Consortium, the analysis was adjusted for sex, 5 principal components of ancestry, and a dummy variable for each cohort. We did not adjust for age in the MIGen Consortium because cases in some cohorts were selected for early-onset myocardial infarction, resulting in age being significantly and inversely associated with the presence of CHD. In the U.K. Biobank, the analysis was adjusted for age, sex, 10 principal components of ancestry, and array type.

All analyses were performed by using R version 3.2.3 software (The R Project for Statistical Computing, Vienna, Austria).

Logistic regression was used to test the association of coronary heart disease (CHD) as an outcome and DNA sequence variant as a predictor, adjusting for sex and principal components of ancestry, with additional adjustment for array type and age in U.K. Biobank. The impact of LPA variation on CHD risk is directly proportional to its effect on circulating Lp(a) levels. Lp(a) = lipoprotein(a).

The goal of this study was to establish the full phenotypic impact of LPA gene variation and to estimate a dose–response curve between genetically altered plasma lipoprotein a (Lp[a]) and risk for coronary heart disease. Estimates were derived in U.K. Biobank using logistic regression, adjusted for age, sex, 10 principal components and array type, with the exception of chronic kidney disease (CKD), which was derived by using summary statistics from the Chronic Kidney Disease Genetics Consortium, and heart failure, which was derived in both UK Biobank and the Cohorts for Heart and Aging Research in Genomic Epidemiology Heart Failure Consortium. One SD genetically lowered Lp(a) level was associated with reduced risk of 5 cardiometabolic diseases. Although the estimate for CKD did not reach Bonferroni-adjusted significance, it was included as a significant outcome because the underlying trait (estimated glomerular filtration rate) was significantly associated with Lp(a) (p = 2 × 10–5). OR = odds ratio.

Although genetically lowered Lp(a) levels were only nominally associated with a 9% lower risk of CKD (OR: 0.91; 95% CI: 0.81 to 1.00; p = 0.043), it was highly significantly associated with the underlying quantitative trait (eGFR), as described later. Genetically lowered Lp(a) level was not associated with diabetes, venous thromboembolism, or atrial fibrillation. To examine if the association of genetically lowered Lp(a) with HF and aortic stenosis was mediated by CHD, we excluded participants with CHD in the U.K. Biobank (n = 4,461). After exclusion, a 1 SD genetic decrease in Lp(a) levels had similar strengths of association with HF (OR: 0.84; 95% CI: 0.66 to 1.07; n = 107,877) and aortic stenosis (OR: 0.70; 95% CI: 0.49 to 0.99; n = 107,877). A sensitivity analysis excluding those with prevalent aortic stenosis (n = 193) yielded a similar strength for the association between a 1 SD decrease in Lp(a) levels and HF (OR: 0.85; 95% CI: 0.72 to 1.02; n = 112,145).

In contrast to the effects of Lp(a) on cardiometabolic disorders, we found no association of genetically lowered Lp(a) with any of 28 different disorders, including 4 gastrointestinal disorders, 3 endocrine disorders, 2 renal/urological disorders, 3 psychiatric disorders, 4 musculoskeletal disorders, 4 respiratory disorders, and 8 different cancers (all p > 0.01) (Figure 2).

Figure 4 provides a dose–response curve for CHD derived from gain and loss-of-function variants at the LPA gene locus. The impact of LPA variation on CHD risk is directly proportional to its effect on circulating Lp(a) levels. The Lp(a)-increasing alleles of common variants rs3798220 and rs10455872, which increased Lp(a) levels by 0.98 and 0.91 SD, respectively, increased risk of CHD by 57% (OR: 1.57; 95% CI: 1.46 to 1.69) and 38% (OR: 1.38; 95% CI: 1.33 to 1.43). Rare synonymous variants, which had no significant effect on Lp(a) levels, also had no significant effect on CHD (OR: 0.98; 95% CI: 0.86 to 1.12). A common loss-of-function variant rs41272114, which decreased Lp(a) levels by 0.62 SD, was associated with a 12% lower risk of CHD (OR: 0.88; 95% CI: 0.84 to 0.93; p = 3.4 × 10–7). Presence of a rare (allele frequency <1%) loss-of-function variant in the LPA gene was associated with a 24% lower risk of CHD (OR: 0.76; 95% CI: 0.59 to 0.98; p = 0.033) (Online Figure 2).

Discussion

To evaluate the phenotypic consequences of genetically lowered Lp(a) levels, we leveraged the following: 1) 4 DNA sequence variants that alter plasma Lp(a) level; 2) individual-level genotype and phenotype data from >100,000 participants in the U.K. Biobank; 3) summary genetic association results from 7 large-scale GWAS; and 4) LPA gene sequences in >15,000 participants. We found that 1 SD genetically lowered Lp(a) was associated with a range of atherosclerosis-related diseases, including CHD, PVD, stroke, HF, and aortic stenosis, but was not associated with 31 other different diseases in a phenome-wide association study.

These data allow for several conclusions. First, using naturally occurring DNA sequence variation, a dose–response relationship between perturbation of Lp(a) and risk for CHD was provided. We examined the effects of both common and rare variants, as well as gain-of-function variants that increase Lp(a) levels and loss-of-function variants that decrease Lp(a) levels. The effects of these different variants on CHD were consistently proportional to their effect on Lp(a). Consistent with 2 recent reports (30,32), a low-frequency loss-of-function variant (rs41272114) and a burden of rare loss-of-function variants in LPA protected against CHD. In combination, these results suggest that greater pharmacological reductions in Lp(a) levels should produce proportionally greater reductions in CHD risk, thus supporting intensive Lp(a) lowering.

Second, these results suggest that Lp(a) inhibition may be a viable therapeutic strategy to prevent a range of diseases beyond CHD. This study extends previous research demonstrating that LPA variants are associated with cardiovascular disease (5,6,11,12,33,34). In a report of up to 12,716 individuals from 35 case-control studies, LPA variants were associated with peripheral arterial disease, ischemic stroke, and coronary artery disease (11). In contrast, in an analysis of 14,465 individuals in the Heart Protection Study, LPA variants were associated with PVD but not with stroke (12). Our results suggest that LPA variants are associated with PVD, stroke, and HF. Furthermore, our report of a significant association with aortic stenosis is consistent with recent analyses demonstrating a significant effect of LPA variants on aortic valve calcification and stenosis (9,10). Inclusion of these diseases in composite endpoints of trials of Lp(a)-reducing therapies (in addition to CHD) may increase the likelihood of a positive trial outcome, highlighting the potential benefits of genetic analyses for trial design and clinical drug development.

Third, a surprising finding of this study was that genetically lowered Lp(a) was associated with a modest but significant improvement in kidney function as assessed by 2 phenotypes—eGFR and prevalence of CKD. This lower risk of CKD may be mediated through a reduction in renal atherosclerotic burden. These findings are consistent with a recent GWAS of metabolites that revealed a strong association between LPA rs10455872 and creatinine levels (35). These results implicate Lp(a) metabolism in the development of CKD.

Study limitations

This study’s major strength was the scale and variety of data sources, which improved our power to detect an effect of genetically lowered Lp(a) on a wide range of diseases and cardiometabolic traits. Our use of the largest available cohorts provided requisite power to demonstrate that genetic Lp(a) lowering was associated with a lower risk of PVD, stroke, HF, and CKD. Our use of the U.K. Biobank allowed us to examine the association of genetic LPA variants across a wide range of noncardiovascular diseases, for which we failed to find an association.

Several study limitations deserve mention. First, our use of a 2-sample design, with exposure estimates from ARIC and outcome estimates from the U.K. Biobank and various GWAS, prevented us from examining whether the effect of LPA variants differed according to baseline levels of Lp(a). Second, our phenome-wide association study might have been underpowered to detect a significant effect of Lp(a) on many of the outcomes. Because the U.K. Biobank develops validated phenotypes and accumulates a greater number of events, a phenome-wide association study may be better-powered to detect an effect on different disorders. Third, we used prevalent events based on a verbal interview with a nurse for our phenome-wide association study of 28 different disorders. Although these events are likely to be of greater specificity than coded hospitalization data, they have not been independently validated. Finally, our population was limited to individuals of European ancestry, and our results may not be generalizable to individuals of different ancestry. Indeed, both Lp(a) levels and the number of Kringle IV domains in Lp(a) have been shown to vary substantially with ancestry, suggesting that the impact of Lp(a) on cardiovascular disease may also differ by ancestry (36).

Conclusions

Genetically decreased Lp(a) was associated with a range of cardiometabolic disorders, including CHD, stroke, PVD, aortic stenosis, HF, and renal dysfunction. Pharmacological lowering of Lp(a) levels may reduce the risk of these disorders.

Perspectives

COMPETENCY IN MEDICAL KNOWLEDGE: A genetic predisposition to lower blood levels of Lp(a) was associated with protection from coronary artery disease, stroke, PVD, aortic stenosis, HF, and CKD but was not associated with type 2 diabetes, gastrointestinal disorders, or specific cancers.

TRANSLATIONAL OUTLOOK: Further research should be conducted to determine whether more intensive lowering of Lp(a) levels results in proportionally greater reductions in cardiovascular risk.

Appendix

Appendix

For an expanded Methods section, as well as supplemental tables and figures, please see the online version of this article.

Footnotes

The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the U.S. Department of Health and Human Services. The REGICOR study was supported by the Spanish Ministry of Economy and Innovation through the Carlos III Health Institute (Red Investigación Cardiovascular RD12/0042, PI09/90506), European Funds for Development (ERDF-FEDER), and by the Catalan Research and Technology Innovation Interdepartmental Commission (2014SGR240). Samples for the Leicester cohort were collected as part of projects funded by the British Heart Foundation (British Heart Foundation Family Heart Study, RG2000010; U.K. Aneurysm Growth Study, CS/14/2/30841) and the National Institute for Health Research (NIHR Leicester Cardiovascular Biomedical Research Unit Biomedical Research Informatics Centre for Cardiovascular Science, IS_BRU_0211_20033). The Munich MI Study is supported by the German Federal Ministry of Education and Research (BMBF) in the context of the e:Med program (e:AtheroSysMed) and the FP7 European Union project CVgenes@target (261123). Additional grants were received from the Fondation Leducq (CADgenomics: Understanding Coronary Artery Disease Genes, 12CVD02). This study was also supported through the Deutsche Forschungsgemeinschaft cluster of excellence “Inflammation at Interfaces” and SFB 1123. The Italian Atherosclerosis, Thrombosis, and Vascular Biology (ATVB) Study was supported by a grant from RFPS-2007-3-644382 and Programma di ricerca Regione-Università 2010-2012 Area 1–Strategic Programmes–Regione Emilia-Romagna. Funding for the exome sequencing project was provided by RC2 HL103010 (HeartGO), RC2 HL102923 (LungGO), and RC2 HL102924 (WHISP). Exome sequencing was performed through RC2 HL102925 (BroadGO) and RC2 HL102926 (SeattleGO). Exome sequencing in ATVB, PROCARDIS, Ottawa, and the Southern German Myocardial Infarction Study was supported by 5U54HG003067 (to Drs. Lander and Gabriel). For a full list of CHARGE-HF (Cohorts for Heart and Aging Research in Genomic Epidemiology–Heart Failure) working group members contributing to this work and for CHARGE-HF acknowledgements, please see PMID 20445134. Mr. Emdin is supported by the Rhodes Trust. Dr. Khera is supported by a John S. Ladue Memorial Fellowship in Cardiology and a KL2/Catalyst Medical Research Investigator Training award (an appointed KL2 award: TR001100) from Harvard Catalyst and has received consulting fees from Merck and Amarin. Dr. Won was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2016R1C1B2007920). Dr. Peloso is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL125751. Dr. Stitziel has received funding from K08HL114642 and R01HL131961; has received a research grant from AstraZeneca; and has received consulting fees from Regeneron. Dr. Farrall has received the British Heart Foundation Centre of Research Excellence, Oxford (RE/13/1/30181), award; the Wellcome Trust core award (090532/Z/09/Z); and funding from the Wellcome Trust Institutional Support Scheme. Dr. Abecasis has served as a consultant for Regeneron. Dr. Rader has received consulting fees from Aegerion Pharmaceuticals, Alnylam Pharmaceuticals, Eli Lilly and Company, Pfizer, Sanofi, and Novartis; is an inventor on a patent related to lomitapide that is owned by the University of Pennsylvania and licensed to Aegerion Pharmaceuticals; and is a cofounder of Vascular Strategies and Staten Biotechnology. Dr. Danesh has served as a consultant for Takeda; has been a member of the Novartis Cardiovascular & Metabolic Advisory Board, the International Cardiovascular and Metabolism Research and Development portfolio committee for Novartis, the Merck Sharp & Dohme UK Atherosclerosis Advisory Board, the Pfizer Population Research Advisory Panel, and the Sanofi Advisory Board; and has received funding from the British Heart Foundation, the BUPA Foundation, diaDexus, European Research Council, the European Union, Evelyn Trust, Fogarty International Centre, GlaxoSmithKline, Merck, the National Heart, Lung, and Blood Institute, the National Health Service Blood and Transplant, the National Institute for Health Research, the National Institute of Neurological Disorders and Stroke, Novartis, Pfizer, Roche, Sanofi, Takeda, The Wellcome Trust, U.K. Biobank, the University of British Columbia, and the U.K. Medical Research Council. Dr. Ardissino has received speaker fees from AstraZeneca, Boehringer Ingelheim, Johnson & Johnson, Bayer, Daiichi-Sankyo, GlaxoSmithKline, Eli Lilly and Company, Boston Scientific, Bristol-Myers Squibb, Menarini Group, Novartis, and Sanofi; and research grants from GlaxoSmithKline, Eli Lilly and Company, Pfizer, and Novartis. Dr. Saleheen has received grants from Pfizer, Regeneron, and the National Institutes of Health. Dr. Kathiresan is supported by a research scholar award from the Massachusetts General Hospital, the Donovan Family Foundation, and R01 HL127564; has received grants from Bayer Healthcare, Aegerion Pharmaceuticals, and Regeneron Pharmaceuticals; has received consulting fees from Merck, Novartis, Sanofi, AstraZeneca, Alnylam Pharmaceuticals, Leerink Partners, Noble Insights, Quest Diagnostics, Genomics PLC, and Eli Lilly and Company; and holds equity in San Therapeutics and Catabasis Pharmaceuticals. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Drs. Emdin and Khera contributed equally to this work.

(2010) Association of genome-wide variation with the risk of incident heart failure in adults of European and African ancestry: a prospective meta-analysis from the cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium. Circ Cardiovasc Genet3:256–266.

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